Computational methods for predicting the interface contacts between proteins
come highly sought after for drug discovery as they can significantly advance
the accuracy of alternative approaches, such as protein-protein docking,
protein function analysis tools, and other computational methods for protein
bioinformatics. In this work, we present the Geometric Transformer, a novel
geometry-evolving graph transformer for rotation and translation-invariant
protein interface contact prediction, packaged within DeepInteract, an
end-to-end prediction pipeline. DeepInteract predicts partner-specific protein
interface contacts (i.e., inter-protein residue-residue contacts) given the 3D
tertiary structures of two proteins as input. In rigorous benchmarks,
DeepInteract, on challenging protein complex targets from the 13th and 14th
CASP-CAPRI experiments as well as Docking Benchmark 5, achieves 14% and 1.1%
top L/5 precision (L: length of a protein unit in a complex), respectively. In
doing so, DeepInteract, with the Geometric Transformer as its graph-based
backbone, outperforms existing methods for interface contact prediction in
addition to other graph-based neural network backbones compatible with
DeepInteract, thereby validating the effectiveness of the Geometric Transformer
for learning rich relational-geometric features for downstream tasks on 3D
protein structures.

本论文介绍了几何变换图形转换器 Geometric Transformer 和预测蛋白质界面接触的终端到终端预测管道 DeepInteract，它通过输入两个蛋白质的 3D 三级结构，预测特定于对象的蛋白质界面接触，成功地超越了现有的界面接触预测方法以及其他图形神经网络骨架，从而验证了 Geometric Transformer 在学习富含关系 - 几何特征方面的有效性。